Key Features

Embed LLMs into real-world applications

Use LangChain to orchestrate LLMs and their components within applications

Grasp basic and advanced techniques of prompt engineering

Book Description

Building LLM Apps delves into the fundamental concepts, cutting-edge technologies, and practical applications that LLMs offer. Ultimately paving the way for the emergence of Large Foundation Models (LFMs) that extend the boundaries of AI capabilities.

The book begins with an in-depth introduction to LLMs. We then explore various mainstream architectural frameworks, including both proprietary models (GPT 3.5/4) and open-source models (Falcon LLM), and analyze their unique strengths and differences. Moving ahead, with a focus on the Python-based, lightweight framework called LangChain. We guide readers through the process of creating intelligent agents capable of retrieving information from unstructured data and engaging with structured data using LLMs and powerful toolkits. Furthermore, the book ventures into the realm of LFMs, which transcend language modeling to encompass various AI tasks and modalities, such as vision and audio.

Whether you are a seasoned AI expert or a newcomer to the field, this book is your roadmap to unlock the full potential of LLMs and forge a new era of intelligent machines.

What you will learn

Core components of LLMs’ architecture, including encoder-decoders blocks, embedding and so on

Get well-versed with unique features of LLMs like GPT-3.5/4, Llama 2, and Falcon LLM

Use AI orchestrators like LangChain, and Streamlit as frontend

Get familiar with LLMs components such as memory, prompts and tools

Learn non-parametric knowledge, embeddings and vector databases

Understand the implications of LFMs for AI research, and industry applications

Customize your LLMs with fine tuning

Learn the ethical implications of LLM-powered applications

Who this book is for

Software engineers and data scientists who want hands-on guidance for applying LLMs to build applications. The book will also appeal to technical leaders, students, and researchers interested in applied LLM topics. We don’t assume previous experience with LLM specifically. But readers should have core ML/software engineering fundamentals to understand and apply the content.